Linear Digressions

We've already talked about neural nets in some detail (links below), and in particular we've been blown away by the way that image recognition from convolutional neural nets can be fed into recurrent neural nets that generate descriptions and captions of the images. Our episode today tells a similar tale, except today we're talking about a blog post where the author fed in wireframes of a website...

Last week we started the story of how you could use a machine learning model in place of a data structure, and this week we wrap up with an exploration of Bloom Filters and Hash Maps. Just like last week, when we covered B-trees, we'll walk through both the "classic" implementation of these data structures and how a machine learning model could create the same functionality.

Jeff Dean and his collaborators at Google are turning the machine learning world upside down (again) with a recent paper about how machine learning models can be used as surprisingly effective substitutes for classic data structures. In this first part of a two-part series, we'll go through a data structure called b-trees. The structural form of b-trees make them efficient for searching, but if yo...

Another installment in our "machine learning might not be a silver bullet for solving medical problems" series. This week, we have a high-profile blog post that has been making the rounds for the last few weeks, in which a neural network trained to visually recognize various diseases in chest x-rays is called into question by a radiologist with machine learning expertise. As it seemingly always do...

The Fourier transform is one of the handiest tools in signal processing for dealing with periodic time series data. Using a Fourier transform, you can break apart a complex periodic function into a bunch of sine and cosine waves, and figure out what the amplitude, frequency and offset of those component waves are. It's a really handy way of re-expressing periodic data--you'll never look at a time...

What better way to kick off a new year than with an episode on the statistics of brewing beer?

We have a throwback episode for you today as we take the week off to enjoy the holidays. This week: what happens when you have a markov chain that generates mashup Kanye West lyrics with Bible verses? Exactly what you think.

When we covered the Word2Vec algorithm for embedding words, we mentioned parenthetically that the word embeddings it produces can sometimes be a little bit less than ideal--in particular, gender bias from our society can creep into the embeddings and give results that are sexist. For example, occupational words like "doctor" and "nurse" are more highly aligned with "man" or "woman," which can crea...

Picking up after last week's episode about maximal margin classifiers, this week we'll go into the kernel trick and how that (combined with maximal margin algorithms) gives us the much-vaunted support vector machine.

Maximal margin classifiers are a way of thinking about supervised learning entirely in terms of the decision boundary between two classes, and defining that boundary in a way that maximizes the distance from any given point to the boundary. It's a neat way to think about statistical learning and a prerequisite for understanding support vector machines, which we'll cover next week--stay tuned!

Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!

DBSCAN is a density-based clustering algorithm for doing unsupervised learning. It's pretty nifty: with just two parameters, you can specify "dense" regions in your data, and grow those regions out organically to find clusters. In particular, it can fit irregularly-shaped clusters, and it can also identify outlier points that don't belong to any of the clusters. Pretty cool!

Want to know what's going on in data science these days?  There's no better way than to analyze a survey with over 16,000 responses that recently released by Kaggle.  Kaggle asked practicing and aspiring data scientists about themselves, their tools, how they find jobs, what they find challenging about their jobs, and many other questions.  Then Kaggle released an interactive summary of the data,...

This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows. If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that crop up in the code when you're trying to go fast. You take shortcuts, hard-code variable values, skimp on the documentation, and generally write not-t...

There are a lot of good resources out there for getting started with data science and machine learning, where you can walk through starting with a dataset and ending up with a model and set of predictions. Think something like the homework for your favorite machine learning class, or your most recent online machine learning competition. However, if you've ever tried to maintain a machine learning...

It's quite common for survey respondents not to be representative of the larger population from which they are drawn. But if you're a researcher, you need to study the larger population using data from your survey respondents, so what should you do? Reweighting the survey data, so that things like demographic distributions look similar between the survey and general populations, is a standard tech...

It's the middle of October, so you've already made two pull requests to open source repos, right? If you have no idea what we're talking about, spend the next 20 minutes or so with us talking about the importance of open source software and how you can get involved. You can even get a free t-shirt! Hacktoberfest main page: https://hacktoberfest.digitalocean.com/#details

In honor of the Chicago marathon this weekend (and due in large part to Katie recovering from running in it...) we have a re-release of an episode about Kalman filters, which is part algorithm part elaborate metaphor for figuring out, if you're running a race but don't have a watch, how fast you're going. Katie's Chicago race report: miles 1-13: light ankle pain, lovely cool weather, the most fun...

Neural networks are complex models with many parameters and can be prone to overfitting.  There's a surprisingly simple way to guard against this: randomly destroy connections between hidden units, also known as dropout.  It seems counterintuitive that undermining the structural integrity of the neural net makes it robust against overfitting, but in the world of neural nets, weirdness is just how...

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level. Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices. We'll go through five key charact...

It's been a busy hurricane season in the Southeastern United States, with millions of people making life-or-death decisions based on the forecasts around where the hurricanes will hit and with what intensity. In this episode we'll deconstruct those models, talking about the different types of models, the theory behind them, and how they've evolved through the years.

There are law enforcement surveillance aircraft circling over the United States every day, and in this episode, we'll talk about how some folks at BuzzFeed used public data and machine learning to find them.  The fun thing here, in our opinion, is the blend of intrigue (spy planes!) with tech journalism and a heavy dash of publicly available and reproducible analysis code so that you (yes, you!) c...

Software engineers are familiar with the idea of versioning code, so you can go back later and revive a past state of the system.  For data scientists who might want to reconstruct past models, though, it's not just about keeping the modeling code.  It's also about saving a version of the data that made the model.  There are a lot of other benefits to keeping track of datasets, so in this episode...

Even as we rely more and more on machine learning algorithms to help with everyday decision-making, we're learning more and more about how they're frighteningly easy to fool sometimes. Today we have a roundup of a few successful efforts to create robust adversarial examples, including what it means for an adversarial example to be robust and what this might mean for machine learning in the future.

This week's episode is just in time for JupyterCon in NYC, August 22-25... Jupyter notebooks are probably familiar to a lot of data nerds out there as a great open-source tool for exploring data, doing quick visualizations, and packaging code snippets with explanations for sharing your work with others. If you're not a data person, or you are but you haven't tried out Jupyter notebooks yet, here's...